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Enhancing Statistical Significance of Backtests by Dr. Ernest Chan at QuantCon 2017

Insufficient historical data is a major hurdle in building a trading model free from data snooping bias. Dr. Chan's talk will discuss several techniques, some borrowed from machine learning, that can alleviate overfitting and enhance the statistical significance of a backtest. Slides can be found at http://bit.ly/2CWjBwz. To learn more about Quantopian, visit https://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.